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Nonlinear clustering in models with primordial non-Gaussianity: the halo model approach

  • Smith, Robert E.
  • Desjacques, Vincent
  • Marian, Laura
Published Article
Publication Date
Aug 08, 2011
Submission Date
Sep 26, 2010
DOI: 10.1103/PhysRevD.83.069901
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We develop the halo model of large-scale structure as an accurate tool for probing primordial non-Gaussianity. In this study we focus on understanding the matter clustering at several redshifts. The primordial non-Gaussianity is modeled as a quadratic correction to the local Gaussian potential, and is characterized by the parameter f_NL. In our formulation of the halo model we pay special attention to the effect of halo exclusion, and show that this can potentially solve the long standing problem of excess power on large scales in this model. The model depends on the mass function, clustering and density profiles of halos. We test these ingredients using a large ensemble of high-resolution Gaussian and non-Gaussian numerical simulations. In particular, we provide a first exploration of how density profiles change in the presence of primordial non-Gaussianities. We find that for f_NL positive/negative high mass halos have an increased/decreased core density, so being more/less concentrated than in the Gaussian case. We also examine the halo bias and show that, if the halo model is correct, then there is a small asymmetry in the scale-dependence of the bias on very large scales, which arises because the Gaussian bias must be renormalized. We show that the matter power spectrum is modified by ~2.5% and ~3.5% on scales k~1.0 h/Mpc at z=0 and z=1, respectively. Our halo model calculation reproduces the absolute amplitude to within 10% and the ratio of non-Gaussian to Gaussian spectra to within 1%. We also measure the matter correlation functions and find similarly good agreement between the model and the data. We anticipate that this modeling will be useful for constraining f_NL from measurements of the shear correlation function in future weak lensing surveys such as Euclid.

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